Phishing abuse recognition in web pages

ABSTRACT

A method of identifying potential phishing abuse images includes: producing a first color map that represents a subset of color values and pixel locations within a base image; producing a second color map that represents color values and pixel locations within a target image; selecting an alignment the first color map with the second color map such that at least some pixel locations of the first color map align with at least some pixel locations of the second color map; determining a measure of color value matching of aligned pixel locations for the selected alignment; and repeating the acts of selecting and determining until a prescribed threshold measure of color value matching is determined for at least one of the selected alignments or until an evaluation limit is reached.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates in general to computer networks, and moreparticularly, to combating occurrences of phishing abuse on theInternet.

2. Description of the Related Art

Phishing is a term used to refer to a practice of luring unsuspectingInternet users to a false web site by using authentic-looking email orweb pages with legitimate enterprise's mark, design or logo, in anattempt to steal confidential information such as passwords, financialor personal data, or launch a virus attack. The term phishing often isused to refer to creation of a replica of a legitimate web site in aneffort to trick fooling unsuspecting Internet users into submittingpersonal or financial information or passwords.

Many web pages include brand logo or trademark or other distinctive orvaluable images that serve to identify a trusted source of goods orservices. Increasingly, such images have become the target of phishingabusers. Phishing often involves the use of popular or well-known imagesto trick web users into believing that a web page is associated with theowner of a trusted source, e.g., a company or individual, generallyassociated with the image. In order to trick such internet users, animage of a brand logo may be incorporated within a web page that has notbeen authorized by the owner of the image. Often, phishing abusers embedunauthorized images within or among other images on the unauthorized webpage. The presence of the unauthorized image is intended to imbue theweb page with an air of legitimacy. Thus, phishing typically involves anunlawful use of proprietary images to perpetuate what amounts to fraudor theft. There is a need to combat such phishing abuse. Morespecifically, there has been a need to identify brand logo or trademarkor other distinctive images appearing on an unauthorized web page evenif such images are embedded within or among other images on the page.

SUMMARY OF THE INVENTION

In one embodiment of the present invention, a method of identifyingpotential phishing abuse images, includes: producing a first color mapthat represents a subset of color values and pixel locations within abase image; producing a second color map that represents color valuesand pixel locations within a target image; selecting an alignment thefirst color map with the second color map such that at least some pixellocations of the first color map align with at least some pixellocations of the second color map; determining a measure of color valuematching of aligned pixel locations for the selected alignment; andrepeating the acts of selecting and determining until a prescribedthreshold measure of color value matching is determined for at least oneof the selected alignments or until an evaluation limit is reached. Oneor more values for the selected alignments can be saved in acomputer-readable medium (e.g., measured values for color matching canbe saved directly or through some related characterization).

According to one aspect of this embodiment, producing the first colormap may include sampling a threshold number of pixel locations of thebase image, wherein at least some pixel locations of the base image areexcluded from the first color map.

According to another aspect, the method may further include: selectingthe base image from a set of candidate base images by prioritizing thecandidate base images according to color similarity with the targetimage.

According to another aspect, selecting the alignment of the first colormap with the second color map may include aligning a first pixel of thefirst color map with a first pixel of the second color map; and aligningadditional pixels of the first color map with additional pixels of thesecond color map according to locations of the additional pixels in thefirst pixel map relative to the first pixel of the first color map andlocations of the additional pixels in the second color map relative tothe first pixel of the second color map.

According to another aspect, wherein determining the measure of colorvalue matching for the alignment may include calculating a sum of colormatching values for pairs of aligned pixels, wherein the color matchingvalues correspond to a degree of similarity in the color values ofaligned pixels.

According to another aspect, the alignment may be a first alignment andthe method may further include: selecting a second alignment of thefirst color map with the second color map, wherein selecting the secondalignment includes: aligning the first pixel of the first color map witha second pixel of the second color map; and aligning additional pixelsof the first color map with additional pixels of the second color mapaccording to locations of the additional pixels in the first color maprelative to the first pixel of the first color map and locations of theadditional pixels in the second color map relative to the second pixelof the second color map. Additionally with respect to this aspect, themethod may further include selecting the second pixel of the secondcolor map by offsetting the first pixel of the second color map along arow or a column of the second pixel map.

Additional embodiments relate to an apparatus for carrying out any oneof the above-described methods, where the apparatus includes a computerfor executing instructions related to the method. For example, thecomputer may include a processor with memory for executing at least someof the instructions. Additionally or alternatively the computer mayinclude circuitry or other specialized hardware for executing at leastsome of the instructions. Additional embodiments also relate to acomputer-readable medium that stores (e.g., tangibly embodies) acomputer program for carrying out any one of the above-described methodswith a computer. In these ways the present invention enables improvedmethods and systems for combating phishing abuse.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustrative flow diagram of a process to produce baseimage information for use to identify potentially illicit usage of abase image in accordance with some embodiments of the invention.

FIGS. 2A-2D are illustrative simplified drawings representing a baseimage (FIG. 2A) together with a color map (FIG. 2B), color map sample(FIG. 2C) and color palette (FIG. 2D) that can be produced for the baseimage in accordance with the process of FIG. 1.

FIG. 3 is an illustrative drawing of a web page scan process that usesbase image information to identify possible phishing abuse images inaccordance with some embodiments of the invention.

FIG. 4 is an illustrative drawing showing details of a process used bythe base image prioritization block of FIG. 3 in accordance with someembodiments of the invention.

FIG. 5 is an illustrative drawing showing details of an image evaluationprocess of block of FIG. 3 in accordance with some embodiments of theinvention.

FIG. 6 is an illustrative drawing showing details of the comparisonprocess of block of FIG. 5 in accordance with some embodiments of theinvention.

FIGS. 7A-7E are illustrative drawings of an example base image (FIG. 7A)and target image (FIG. 7B) and three example alignments (FIGS. 7C-7E) ofthe two images during the comparison process of FIG. 6 in accordancewith some embodiments of the invention.

FIG. 8 is an illustrative block level diagram of a computer system thatcan be programmed to implement processes involved identifying potentialphishing abuse images within a web page in accordance with embodimentsof the invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

The following description is presented to enable any person skilled inthe art to make and use a method and system to identify potentialphishing abuse practiced over the Internet in accordance withembodiments of the invention, and is provided in the context ofparticular applications and their requirements. Various modifications tothe preferred embodiments will be readily apparent to those skilled inthe art, and the generic principles defined herein may be applied toother embodiments and applications without departing from the spirit andscope of the invention. Moreover, in the following description, numerousdetails are set forth for the purpose of explanation. However, one ofordinary skill in the art will realize that the invention might bepracticed without the use of these specific details. In other instances,well-known structures and processes are shown in block diagram form inorder not to obscure the description of the invention with unnecessarydetail. Thus, the present invention is not intended to be limited to theembodiments shown, but is to be accorded the widest scope consistentwith the principles and features disclosed herein.

A base image, for example, may comprise a brand logo or trademark orother distinctive or valuable image associated with a trusted source ofgoods or services. A system in accordance with embodiments of theinvention may be employed to evaluate a huge number of web pages forillicit usage of any of a large number of base images. For example, aweb crawler may be employed to crawl the web and to identify perhapsmillions of web pages to be evaluated to determine for each such webpage, whether there is an illicit use of any one or more of thousands ofdifferent base images. A search for potential phishing abuse on such alarge volume of web pages necessitates an efficient system and method toidentify and evaluate potentially illicit images.

FIG. 1 is an illustrative flow diagram of a process 100 to produce baseimage information for use to identify potentially illicit usage of abase image in accordance with some embodiments of the invention. Thisprocess may be performed for each of many base images, perhaps manythousands or even million of base images, in order to produce base imageinformation useful for efficiently identifying potential phishing abuse.A file system 102 is provided includes image files 104 for a pluralityof base images, perhaps many thousands, that are at risk of phishingabuse. A given image file 104 typically contains computer readable codeused by a computer system to generate a display of a base image. A baseimage and its corresponding file, for instance, may be created usinggraphic image creation tools such as Adobe® Photoshop, Corel®Photo-Paint, Corel® Paint Shop Pro, and GIMP (Gnu Image ManipulationProgram). Base image information is produced for each such image so thatsuch information can be used to detect possible phishing abuse.

FIGS. 2A-2D are illustrative simplified drawings representing a baseimage (FIG. 2A) together with a color map (FIG. 2B), color map sample(FIG. 2C) and color palette (FIG. 2D) that can be produced for the baseimage in accordance with the process of FIG. 1. It will be appreciatedof course that a greatly simplified base image that includes only forty(40) pixels is shown in FIG. 2A for the purpose of illustration, andthat an actual base image may have several hundred or even severalthousand pixels, each with a corresponding color value and pixellocation. The color and arrangement of these pixels determines theoverall appearance of the base image. FIG. 2A is an illustrative drawingof an example 8×5 pixel array in which pixels encompassed within anexample base image are labeled with letters “A”, “B” or “C” representingdifferent color values. FIG. 2B is an illustrative drawing representinga color map comprising a series of pixel color information valuesassociated with two-dimensional locations of pixels within the baseimage. FIG. 2C is an illustrative drawing representing a color mapsample that includes color values for a subset of the pixel locationsrepresented within the color map of FIG. 2B. FIG. 2D is an illustrativedrawing representing a color palette that indicates the range of colorscontained within the base image of FIG. 2A.

Referring again to FIG. 1, a base image file 104 obtained from a filesystem 102 is provided to a data collector 106, which is a centralmechanism that collects relevant data for the base image and providestemporary storage in memory before sending the data to a storage device116. A color map generator block 108 interacts with the data collector106 and produces a color map that comprises color value information andvertical and horizontal position information for each pixel locationwithin the base image. In some embodiments, color values are stored as a24-bit hexadecimal value, in the format “RRGGBB”—two bytes for red,green and blue channels, which is temporarily stored in the datacollector 106.

The following is a simplified illustrative example of a base imagecomprising only four pixels, where the values represent hexadecimalcolor values for the pixels:

[0xffffff, 0xffffff]

[0x000000, 0x000000]

The dimensions of this example base image are 2×2—two pixels in width bytwo pixels in height.

The color map generator block 108 converts this simple matrix into aone-dimensional series of values that represent pixel color information:

[0xffffff, 0xffffff, 0x000000, 0x000000]

Dimension information, such as the image width and height, is collectedduring the image acquisition process by the data collector 106 and sentto the storage device 116 in additional data fields along with the colormap. Based on image with and height, one can reconstruct the originalimage structure, including, for example, values for vertical andhorizontal coordinates of the pixels. These coordinate values are thenused in the scanning and matching process so that corresponding pixelsare matched when scanning the arrays. Storing the color information as aseries of values simplifies storage and scanning operations. Becauseimage dimensions have also been stored, no information about the imageis lost during this conversion step.

If a background color for the base image is specified, then color valueswithin the base image color map that match the background color valuecan be removed. Typically, background color is white, so assuming thatthe color value [0xffffff] corresponds to the color white, the series ofcolor map values becomes

[ . . . 0x000000, 0X000000]

The position of all base image pixels is preserved since backgroundcolor pixels are replaced by blank entries (e.g., null entries) withoutdisturbing the pixels' position coordinates (e.g., horizontal/vertical,x/y). When background color is filtered out in this manner, this cangreatly improve scanning accuracy and performance. For instance, in someembodiments, each time position and color values match between a baseand target image, a “similarity score” is incremented by one. Removal ofbackground color values from the base image sample prevents incrementingof such similarity score based upon matching that is merely indicativeof a target image's matching with a base image's background color. Thus,filtering out such background color information avoids skewing asimilarity score that otherwise might arise through background colormatching. Removal of such background color information also reduces thenumber of pixels to be scanned, sometimes by 50% or more, when allirrelevant (background) pixels are removed.

Referring once again to FIG. 2A, each example pixel in the array isassigned a unique numeric value indicative of its position. The baseimage encompasses pixel locations that contain color values (i.e., “A”,“B” or “C”). Referring to the illustrative drawing of FIG. 2B, the colormap generator 108 produces the series color values that are orderedbased upon pixel locations of color values within the base image. Forexample, the color value A₁₂ in FIG. 2B indicates that color “A” (whichmay be represented in a 24 bit RRGGBB format) associated with location12, which corresponds to location (4, 4) in the base image map of FIG.2A.

Referring again to FIG. 1, a color map sample generator block 110interacts with the color map generator 108 to access the color map andto produce a color map sample for the base image. The color map samplegenerator 110 then interacts with the data collector 106 by sending asample data set to the data collector 106, which then sends the sampledata set to the storage device 116. In some embodiments, the color mapsample generator 110 selects a random sampling of color valueinformation entries from the series of color values produced by thecolor map generator 108, which is stored temporarily in the datacollector 106. A minimal number of required pixels is determined by aformula for normal approximation to the hypergeometric distribution,which is typically used in statistics for sampling a ‘small population’:n=(N*z ² *p*q)/(E ²*(N−1)+z ² *p*q)where, n is the required sample size; N is the population size (e.g.,total number of pixels); p and q are the population proportions; z is avalue that specifies a desired level of confidence. E sets an accuracyof sample proportions. (See, for example, “Sampling from SmallPopulations” by Evan Morris, downloaded from Internet siteuregina.ca/˜morrisev.)

A random sample of pixel color value/location pairs is selected from thecolor map (e.g., by using a random number generator to select elementsof the color map). These sample color value/location pairs (base imagepixel information) are compared with pixels of target images, asexplained below with reference to FIGS. 3-7, to determine whether anyone or more of the target images are suspected to represent illicitphishing images. A ‘small population’ formulation is a useful mechanismto determine an optimal sample size since in most cases base imagescontain no more than a few thousand pixels. Given the small populationsample size approach, typically only about 20% of foreground pixels(e.g., all pixels not corresponding to a predetermined background color)need be compared with target image pixels in each scanning pass in orderto identify potential phishing abuse, while maintaining a relativelysmall statistical margin of error of perhaps about only 5%. The use ofsuch a smaller sampling of base image pixel information vastly improvesscanning performance since fewer pixels need be compared.

Referring to FIG. 2C, there is shown an example color map sampleproduced by the color sample generator 110 in which color values havebeen selected at random from the series of color values of FIG. 2B (asindicated by an “S” above selected pixels). Note that the relativeordering of color values in the sample is retained, and that samplevalues need not be contiguous. In other words, color values are selectedfrom random pixel locations within the base image of FIG. 2A.

Referring to FIG. 1, a color palette generator 112 determines the rangeof colors present in the base image. Specifically, the color palettegenerator 112 determines the set of unique color values in the color mapproduced by the color map generator 108, which is stored in memory inthe data collector 106 The color palette provides criteria for athreshold determination of similarity between the base image and atarget image as explained more fully below.

Referring to FIG. 2D, there is shown an example color palette producedby the color palette generator 112 for the base image of FIG. 2A. Threecolor values, “A”, “B” and “C” are included in the base image. In someembodiments, each is represented as a unique 24 bit value in the RRGGBBformat described above. Therefore, those three color values are includedin the color map of FIG. 2D.

The base image information, including color map information produced bygenerator 108, color map sample information produced by generator 110and color palette information produced by generator 112, is provided tothe data collector 106, which sends the complete data set to the storagedevice 116. Image meta-data 114 such as width and height is calculatedduring the image acquisition process to identify the base imageinformation. The base image information together with themeta-information 114 is stored in a database 116. In some embodiments,the base image information is stored in the form of text strings in adatabase such as an SQlite database, for example.

FIG. 3 is an illustrative drawing of a web page scan process 300 thatuses base image information to identify possible phishing abuse imagesin accordance with some embodiments of the invention. A web page 302 isselected for investigation. Image asset collector block 304 collects oneor more target images from the selected page. A web page may includenumerous images, and individual images may be gathered and targeted forinvestigation, for example, using existing methods such as by searchingfor an “img” tag in the page HTML code using regular expressions.

Target selection block 306 selects an image from among the one or morecollected images to become the next target to be evaluated. Color mapgenerator block 308 accesses color information associated with thecurrently selected target image and assigns a color palette to suchtarget image. Image data collector block 310 collects color paletteinformation assigned to the current target image. Base imageprioritization block 312 prioritizes base images represented within thefile system 102 based upon the degree of similarity of their respectivecolor palettes (e.g., FIG. 2D) and the color palette of the currentlyselected target image gathered by block 310. As explained below, thisprioritization based upon color palette similarity also can be used toas an initial threshold determination to identify base images that areso dissimilar to the currently selected target image, that no furtherevaluation is merited.

Decision block 314 selects a base image according to a priority orderdetermined by block 312 for evaluation against the currently selectedtarget image. If decision block 314 determines that there are noadditional base images to be evaluated, then the process proceeds todecision block 316, which determines whether there are additional targetimages from the selected web page that have not yet been evaluated. Ifdecision block 316 determines that there are additional target images tobe evaluated, then the process loops back to block 306, which selects anext image from among the one or more collected images to be evaluated.If decision block 316 determines that there are no more target images tobe evaluated, then the process ends.

On the other hand, if decision block 314 determines that there areadditional base images to be evaluated then image evaluation block 318accesses base image information from a base cache 319 in the database116 and compares it with the currently selected target image. Decisionblock 320 determines whether the currently selected base image meets aprescribed level of similarity to the currently selected target image soas to indicate that such selected target image should be identified as apossible phishing image. If decision block 320 determines a prescribedthreshold level of similarity then a positive match is identified, andthe selected web page is identified as a suspected phishing page. Ifdecision block 320 determines that the currently selected image does notmeet the prescribed threshold level of similarity then the process loopsback to block 312, and decision block 314 selects a next target image inthe determined prioritization order.

FIG. 4 is an illustrative drawing showing details of a process used bythe base image prioritization block 312 of FIG. 3 in accordance withsome embodiments of the invention. As described above with reference toFIG. 3, color palette generator 308 generates color palette data for agiven target image object 402 currently selected by block 306 forevaluation and provides color palette data to the data collector block310. For each of the multiple base images to be evaluated for phishingabuse, color palette information 408 produced in accordance with theprocess of FIG. 1 is obtained from database 116 and is provided to baseimage data block 410. Priority scoring block 412 determines a priorityscore for each base image by extracting color palette information fromblock 310 and comparing that information with color palette informationfrom block 410 for each base image. If a target image has a color thatmatches one of base image colors, the score for that base image asassociated with the given target image is incremented by one; if atarget image has two colors that match two base image colors, the scoreis incremented by two, etc. In this manner, a score representing apercentage of matching colors is calculated between the given targetimage and each base image, and a corresponding priority is assigned as ascore for each base image. The result is a series of scores—one per baseimage. The scores are sorted by highest score on top and are stored in apriority score array 414, which is accessed by decision block 314 ofFIG. 3 to determine the order in which to evaluate target images.Prioritization in this manner ensures that the most likely base imagecandidates for phishing abuse are checked first, which reduces thenumber of required scan cycles. Moreover, base images with a priorityscore below some prescribed minimum threshold are not included in thepriority score array 414 and are not evaluated, speeding the overallprocess. In some exemplary cases, the score for matching colors can beweighted (e.g., by the number of pixels of a certain color or therelative positions of the pixels of a certain color).

FIG. 5 is an illustrative drawing showing details of an image evaluationprocess of block 318 of FIG. 3 in accordance with some embodiments ofthe invention. Referring to FIG. 3, decision process 314 selects thebase image with the highest overall priority score that has not yet beenevaluated against a currently selected target image. As will beappreciated from FIG. 3, the target image is compared against all baseimages that have a minimum qualifying priority score, starting with thehighest scored. More specifically, data collector 502 obtains a targetimage object 504 selected from the web page under investigation andobtains base image information 506 from database 116. Background filterblock 508 removes background color values from the target image (incorrespondence to the removal of background color values from the baseimage as described above with reference to FIG. 1). Block 510 performs acomparison of the currently selected target image to the currentlyselected base image in order to assess a level of similarity between thetwo. Similarity measurement results are collected in block 512 for useby decision block 320.

FIG. 6 is an illustrative drawing showing details of the comparisonprocess of block 510 of FIG. 5 in accordance with some embodiments ofthe invention. The currently selected target image 602 and a base imagecolor map (e.g., FIG. 2B) of the currently selected base image 604 areprovided to an initial alignment block 606, which establishes an initialalignment of pixel color values of the target image object with pixelcolor values of the base image color map. The initial alignment is doneby taking the first n pixels, where n is the pixel width of the baseimage so that these n pixels would correspond to the first row of thebase image, and aligning them with the first n pixels of the targetimage, and continuing row by row (e.g, as described below with referenceto FIG. 7). Comparison block 610 compares pixel color values of the baseimage color map sample (e.g., FIG. 2C) with pixel values of the targetimage as currently aligned. That is, only color map sample values areused in the comparison, where the sample size can be chosen as describedabove. Decision block 320 makes a determination as to whether the numberof matching color values meets a threshold value indicative of therebeing a match between the currently selected target image and theselected base image. (Note that decision block 320 is a part of block510, which is embedded within the process of FIG. 3.) If decision block320 makes a determination that the threshold has been met, then block322 flags the web page containing the currently selected base image as asuspected phishing abuse web page, as explained above with reference toFIG. 3. If decision block 320 determines that the threshold has not beenmet, then decision block 616 determines whether the current alignmentmeets an alignment limit. If decision block 616 makes a determinationthat the alignment limit has been met, then the process returns to block312 shown in FIG. 3. If decision block 616 determines that the alignmentlimit has not yet been met, then block 618 shifts the relative positionof the target image and the base images to a next prescribed alignment,and the process loops back to the comparison block 610.

More particularly, the shift alignment block 618 implements a scanprocess in which alignment of the selected base image is shiftedincrementally relative to the target image. At each incremental shiftposition, comparison block 610 determines the number of base image pixellocations that have associated color values that match color values oftarget image pixel values that they are aligned with. The comparisoninvolves a pixel value scanning process in which the color value of onepixel location after another in the base image is compared with thecolor value of the target image pixel location currently aligned withit. At each incremental shift position, the threshold limit decisionblock 320 determines whether the number of matches meets a prescribedthreshold limit. It will be appreciated that multiple pixels locationsat a time may be evaluated simultaneously (in parallel) during the scanoperation consistent with the principles of the invention.

For example, in some embodiments, the first scan pass begins bycomparing the color of first pixel in target image with color of firstpixel in base image by applying a XOR operation:

baseColorMap[0][0]^targetColorMap[0][0]

A result of “0” indicates a match and a running tally is incremented byone. Subsequently the scan continues such that each n-th pixel of targetimage is compared against the corresponding n-th pixel of base image andthe tally is updated. This continues until the last pixel is reached. Asdiscussed above, the pixel comparisons are typically only made at valuescorresponding to the base color map sample so that the indices of thecolor maps are accessed accordingly.

In a next pass, for example, n is offset by one (1) (the offset value isconfigurable) in the target image, so that target pixel n+1 is comparedto base pixel n. With each pass, the base color map continues toincrement the offset by one (1) pixel, until last element of base colormap is offset far enough to match against the last element of targetcolor map. Note: it is not required to always offset by one (1) pixel toget sufficiently good results. Tests have shown that even ten (10) pixeloffsets can produce good results, while greatly speeding up the scanningoperation. This offset scanning technique ensures that a base image canbe accurately detected if it is present in a target image, which maycontain other image elements, such as in a composite image.

Each pass is scored separately. Once a threshold score (a configurablevalue) is reached, scanning stops and block 322 reports a “positivematch” result. If the minimum score is not achieved, then the process ofFIG. 3 moves to the next base image and then to the next target image.

The process of FIG. 6 will be better understood through the followingexplanation of the illustrative drawings of FIGS. 7A-7E. FIGS. 7A-7E areillustrative drawings of an example base image (FIG. 7A) and targetimage (FIG. 7B) and three example alignments (FIGS. 7C-7E) of the twoimages during the comparison process of FIG. 6 in accordance with someembodiments of the invention. FIG. 7A is an illustrative drawing of anexample base image color map (e.g., corresponding to the example baseimage of FIG. 2A). Specifically, the grey shaded regions 702 representpixel locations having base image color values. The ‘white’ spaces 704are background. FIG. 7B is an illustrative drawing of an example targetimage color map for a target image collected from a web page that isunder investigation. Specifically, the grey shaded regions representpixel locations having target image color values.

In FIGS. 7A-7E the pixel dimension are relatively small for illustrativepurposes. In FIG. 7A the base image includes a rectangular array ofeight (8) pixels in the horizontal direction and four (4) pixels in thevertical direction, so that there are thirty-two (32) pixels in the baseimage with twenty (20) pixels in the grey-shaded region 702 (denotingbase image color values) and twelve (12) pixels in the white region 704(denoting background). In FIG. 7B the target image includes arectangular array of twelve (12) pixels in the horizontal direction andeight (8) pixels in the vertical direction, so that there are ninety-six(96) pixels in the target image, Although FIGS. 7A-7E illustrate thealignment process for the “entire” base image (FIG. 7A), thecalculations (e.g, XOR operations) for pixel comparisons only need to bedone for pixels corresponding to the base color map sample (e.g, FIG.2C).

FIG. 7C is an illustrative drawing of an initial alignment selected byblock 606 of FIG. 6 in which alignment of the base image color map andthe target image color map is characterized as: row 1, offset 0. In thisexample, ‘row 1’ signifies that the top pixel locations of the baseimage are aligned with the top pixel locations of the target image, and‘offset 0’ signifies that the far left pixel locations of the base imagecolor map are aligned with the far left pixel locations of the targetimage. FIG. 7D is an illustrative drawing of a first shifted alignmentselected by block 618 of FIG. 6 in which alignment of the base imagecolor map and the target image color map is characterized as: row 1,offset 4 pixels. In this example, ‘offset 4 pixels’ signifies that thebase image color map has been shifted to the right relative to thetarget image by four pixel locations. FIG. 7E is an illustrative drawingof a third shifted alignment selected by block 618 of FIG. 6 in whichalignment of the base image color map and the target image color map ischaracterized as: row 2, offset 0. In this example, ‘row 2’ signifiesthat the base image color map has been offset vertically downward byfour (4) pixel locations pixel locations relative to the target imagecolor map.

The row 2, offset 0 alignment is an example of an alignment limit inthat the base image color map has reached the end of the target imagecolor map. If the evaluation process does not end sooner due to adetermination that the similarity measure during at least one of thealignments meets a threshold level, then the evaluation process endswhen an evaluation limit is reached. One such evaluation limit is thealignment limit. Certainly, further valuation makes little sense oncesubstantially the entire target image already has been evaluated.Alternatively, an evaluation limit based upon factors such as theduration or extent of the evaluation process may be based upon adifferent metric such as the number of scan increments or some othertimeout mechanism, for example.

The dark shaded regions 706 of FIGS. 7C-7E represent pixel locationshaving matching color values as can be determined by block 610 of FIG.6. In this example, the alignment of FIG. 7C results in a determinationthat five (5) pixel locations have matching color values. The alignmentof FIG. 7D results in a determination that four (4) pixel locations havematching color values. The alignment of FIG. 7E results in adetermination that twenty (20) pixel locations have matching colorvalues. Since the base image (FIG. 7A) consists of twenty (20) pixelswith base image color values 702, “20” is the maximum possible score,and no further scanning is needed.

The number of pixel locations having matching color values serves as ameasure of similarity between a base image and a target image. The morepixel locations having color matches the higher the likelihood that thetarget image resides on a web page involved with illicit phishing abuse.If some threshold number of pixel locations match, then decision block320 of FIGS. 3 and 6 makes a determination to flag the suspect web pagefor additional investigation, perhaps by a human reviewer. In thisexample, the number of matches in each of FIGS. 7C-7D is too few todetermine that phishing is suspected, but the number of matches in FIG.7E clearly indicates phishing since there is a complete match. Athreshold number of matches typically would be set at some level lessthan the complete overlap match shown in FIG. 7E. As noted above, thesizes of the base image color map and the target color map are typicallymuch larger than the dimensions shown in this example and the actualcomparisons are done on a reduced pixel set corresponding to the baseimage color map sample (FIG. 2C).

FIG. 8 is an illustrative block level diagram of a computer system 800that can be programmed to implement processes involved identifyingpotential phishing abuse images within a web page in accordance withembodiments of the invention. In particular, the process of FIG. 1 andof FIGS. 3-6 may be implemented as one or more computer programs encodedin computer readable media to direct the system 800 to perform suchprocesses. Computer system 800 can include one or more processors, suchas a processor 802. Processor 802 can be implemented using a general orspecial purpose processing engine such as, for example, amicroprocessor, controller or other control logic. In the exampleillustrated in FIG. 8, processor 802 is connected to a bus 804 or othercommunication medium.

Computing system 800 also can include a main memory 806, preferablyrandom access memory (RAM) or other dynamic memory, for storinginformation and instructions to be executed by processor system 802.Main memory 806 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 502. Computer system 800 can likewise include aread only memory (“ROM”) or other static storage device coupled to bus804 for storing static information and instructions for processor system802. The main memory 806 and the storage devices 808 may store data suchas base image information for a multiplicity of base images, forexample. The main memory 806 and the storage devices 808 may storeinstructions such as instructions to implement an incremental scanningprocess in which a base image is compared with a target image todetermine a measure of similarity between the two, for example. The mainmemory 806 and the storage devices 808 also may store instructions tobase image information for a multiplicity of base images, for example.

The computer system 800 can also includes information storage mechanism808, which can include, for example, a media drive 510 and a removablestorage interface 812. The media drive 810 can include a drive or othermechanism to support fixed or removable storage media 814. For example,a hard disk drive, a floppy disk drive, a magnetic tape drive, anoptical disk drive, a CD or DVD drive (R or RW), or other removable orfixed media drive. Storage media 814 can include, for example, a harddisk, a floppy disk, magnetic tape, optical disk, a CD or DVD, or otherfixed or removable medium that is read by and written to by media drive810. Information storage mechanism 808 also may include a removablestorage unit 816 in communication with interface 812. Examples of suchremovable storage unit 816 can include a program cartridge and cartridgeinterface, a removable memory (for example, a flash memory or otherremovable memory module). As these examples illustrate, the storagemedia 814 can include a computer useable storage medium having storedtherein particular computer software or data.

The computer system 800 also includes a display unit 818 that can beused to display information such as a base image or a target image or aweb page. Moreover, the display unit can be used to display similaritymeasures for different comparisons between target images and baseimages.

In this document, the terms “computer readable medium,” “computerprogram medium” and “computer useable medium” are used to generallyrefer to media such as, for example, memory 806, storage device 808, ahard disk installed in hard disk drive 810. These and other variousforms of computer useable media may be involved in carrying one or moresequences of one or more instructions to processor 802 for execution.Such instructions, generally referred to as “computer program code”(which may be grouped in the form of computer programs or othergroupings), when executed, enable the computing system 800 to performfeatures or functions of the present invention as discussed herein.

The foregoing description and drawings of preferred embodiments inaccordance with the present invention are merely illustrative of theprinciples of the invention. Various modifications can be made to theembodiments by those skilled in the art without departing from thespirit and scope of the invention, which is defined in the appendedclaims.

1. A method of identifying potential phishing abuse images, comprising:producing a first color map that represents a subset of color values andpixel locations within a base image; producing a second color map thatrepresents color values and pixel locations within a target image;selecting an alignment the first color map with the second color mapsuch that at least some pixel locations of the first color map alignwith at least some pixel locations of the second color map; determininga measure of color value matching of aligned pixel locations for theselected alignment; repeating the acts of selecting and determininguntil a prescribed threshold measure of color value matching isdetermined for at least one of the selected alignments or until anevaluation limit is reached; and saving one or more values for theselected alignments in a computer-readable medium.
 2. A method accordingto claim 1, wherein producing the first color map includes sampling athreshold number of pixel locations of the base image, wherein at leastsome pixel locations of the base image are excluded from the first colormap.
 3. A method according to claim 1, further comprising: selecting thebase image from a set of candidate base images by prioritizing thecandidate base images according to color similarity with the targetimage.
 4. A method according to claim 1, wherein selecting the alignmentof the first color map with the second color map includes: aligning afirst pixel of the first color map with a first pixel of the secondcolor map; and aligning additional pixels of the first color map withadditional pixels of the second color map according to locations of theadditional pixels in the first pixel map relative to the first pixel ofthe first color map and locations of the additional pixels in the secondcolor map relative to the first pixel of the second color map.
 5. Amethod according to claim 1, wherein determining the measure of colorvalue matching for the alignment includes calculating a sum of colormatching values for pairs of aligned pixels, wherein the color matchingvalues correspond to a degree of similarity in the color values ofaligned pixels.
 6. A method according to claim 1, wherein the alignmentis a first alignment and the method further comprises: selecting asecond alignment of the first color map with the second color map,wherein selecting the second alignment includes: aligning the firstpixel of the first color map with a second pixel of the second colormap; and aligning additional pixels of the first color map withadditional pixels of the second color map according to locations of theadditional pixels in the first color map relative to the first pixel ofthe first color map and locations of the additional pixels in the secondcolor map relative to the second pixel of the second color map.
 7. Amethod according to claim 6, further comprising selecting the secondpixel of the second color map by offsetting the first pixel of thesecond color map along a row or a column of the second pixel map.
 8. Anapparatus for identifying potential phishing abuse images, the apparatuscomprising a computer for executing computer instructions, wherein thecomputer includes computer instructions for: producing a first color mapthat represents a subset of color values and pixel locations within abase image; producing a second color map that represents color valuesand pixel locations within a target image; selecting an alignment thefirst color map with the second color map such that at least some pixellocations of the first color map align with at least some pixellocations of the second color map; determining a measure of color valuematching of aligned pixel locations for the selected alignment; andrepeating the acts of selecting and determining until a prescribedthreshold measure of color value matching is determined for at least oneof the selected alignments or until an evaluation limit is reached. 9.An apparatus according to claim 8, wherein the computer includes aprocessor with memory for executing at least some of the computerinstructions.
 10. An apparatus according to claim 8, wherein thecomputer includes circuitry for executing at least some of the computerinstructions.
 11. An apparatus according to claim 8, wherein producingthe first color map includes sampling a threshold number of pixellocations of the base image, wherein at least some pixel locations ofthe base image are excluded from the first color map.
 12. An apparatusaccording to claim 8, wherein the computer further includes computerinstructions for: selecting the base image from a set of candidate baseimages by prioritizing the candidate base images according to colorsimilarity with the target image.
 13. An apparatus according to claim 8,wherein selecting the alignment of the first color map with the secondcolor map includes: aligning a first pixel of the first color map with afirst pixel of the second color map; and aligning additional pixels ofthe first color map with additional pixels of the second color mapaccording to locations of the additional pixels in the first pixel maprelative to the first pixel of the first color map and locations of theadditional pixels in the second color map relative to the first pixel ofthe second color map.
 14. An apparatus according to claim 8, whereindetermining the measure of color value matching for the alignmentincludes calculating a sum of color matching values for pairs of alignedpixels, wherein the color matching values correspond to a degree ofsimilarity in the color values of aligned pixels.
 15. An apparatusaccording to claim 8, wherein the alignment is a first alignment and thecomputer further includes computer instructions for selecting a secondalignment of the first color map with the second color map, whereinselecting the second alignment includes: aligning the first pixel of thefirst color map with a second pixel of the second color map; andaligning additional pixels of the first color map with additional pixelsof the second color map according to locations of the additional pixelsin the first color map relative to the first pixel of the first colormap and locations of the additional pixels in the second color maprelative to the second pixel of the second color map.
 16. Anon-transitory computer-readable medium that stores a computer programfor identifying potential phishing abuse images, wherein the computerprogram includes instructions for: producing a first color map thatrepresents a subset of color values and pixel locations within a baseimage; producing a second color map that represents color values andpixel locations within a target image; selecting an alignment the firstcolor map with the second color map such that at least some pixellocations of the first color map align with at least some pixellocations of the second color map; determining a measure of color valuematching of aligned pixel locations for the selected alignment; andrepeating the acts of selecting and determining until a prescribedthreshold measure of color value matching is determined for at least oneof the selected alignments or until an evaluation limit is reached. 17.A non-transitory computer-readable medium according to claim 16, whereinproducing the first color map includes sampling a threshold number ofpixel locations of the base image, wherein at least some pixel locationsof the base image are excluded from the first color map.
 18. Anon-transitory computer-readable medium according to claim 16, whereinthe computer program further includes instructions for: selecting thebase image from a set of candidate base images by prioritizing thecandidate base images according to color similarity with the targetimage.
 19. A non-transitory computer-readable medium according to claim16, wherein selecting the alignment of the first color map with thesecond color map includes: aligning a first pixel of the first color mapwith a first pixel of the second color map; and aligning additionalpixels of the first color map with additional pixels of the second colormap according to locations of the additional pixels in the first pixelmap relative to the first pixel of the first color map and locations ofthe additional pixels in the second color map relative to the firstpixel of the second color map.
 20. A non-transitory computer-readablemedium according to claim 16, wherein determining the measure of colorvalue matching for the alignment includes calculating a sum of colormatching values for pairs of aligned pixels, wherein the color matchingvalues correspond to a degree of similarity in the color values ofaligned pixels.